An enhanced multi-view human action recognition system for virtual training simulator

Beom Kwon, Junghwan Kim, Sanghoon Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Virtual military training systems have received considerable attention as a possible substitute for conventional real military training. In our previous work, human action recognition system using multiple Kinects (HARS-MK) has been implemented as a prototype of virtual military training simulator. However, the classification accuracy of HARS-MK is not enough to be utilized for virtual military training simulator. In addition, the experiments are carried out under just two simple action types; walking and crouching walking. In order to overcome these limitations, in this paper, we propose an enhanced multi-view human action recognition system (EM-HARS). Compared to HARS-MK, in EM-HARS, feature extractor is enhanced by employing covariance descriptor. In addition, the feasibility test of EM-HARS is conducted under various human actions including military training actions which are newly captured. The experiment results show that EM-HARS achieves higher classification accuracy than that of HARS-MK.

Original languageEnglish
Title of host publication2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9789881476821
DOIs
Publication statusPublished - 2017 Jan 17
Event2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 - Jeju, Korea, Republic of
Duration: 2016 Dec 132016 Dec 16

Publication series

Name2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016

Other

Other2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016
CountryKorea, Republic of
CityJeju
Period16/12/1316/12/16

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems
  • Signal Processing

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  • Cite this

    Kwon, B., Kim, J., & Lee, S. (2017). An enhanced multi-view human action recognition system for virtual training simulator. In 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016 [7820895] (2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, APSIPA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/APSIPA.2016.7820895